Congenital heart defects are the commonest class of birth defect and are associated with a wide range of anatomic lesions which are frequently life-threatening without surgery early in life. The success of these interventions means that there are now more adults than children with congenital heart disease (CHD), but many of these patients are at risk of adverse ventricular remodeling and heart failure. Managing these patients and predicting when to intervene are important clinical decisions and cardiac MRI exams every few years are common. However how to use the structural and functional data from these studies is complicated by the atypical and widely varying ventricular shapes in many CHD such as repaired Tetrology of Fallot. Here, I describe how ventricular shape atlases derived from principal component analysis of parametric shape models can be used to understand patient variation and identify potential markers of adverse ventricular remodeling in CHD .

Underlying ventricular modeling processes are myocyte signaling pathways, many of which are mechanosensitive. Genome scale data such as RNA -seq provide a way to experimentally validate the predictions of cell signaling models. In a model of myocyte mechanosignaling, we were able to correctly predict the responses over over 70% of approximately 800 genes to longitudinal and transverse stretch. However, uncertainty in underpowered transcriptomic data sets is a significant impediment to more stringent model validation and optimization.